關(guān)聯(lián)規(guī)則挖掘綜述.doc
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關(guān)聯(lián)規(guī)則挖掘綜述,頁數(shù) 9字數(shù) 7791 摘要本文介紹了關(guān)聯(lián)規(guī)則挖掘的研究情況，提出了關(guān)聯(lián)規(guī)則的分類方法，對一些典型算法進行了分析和評價，指出傳統(tǒng)關(guān)聯(lián)規(guī)則衡量標(biāo)準(zhǔn)的不足，歸納出關(guān)聯(lián)規(guī)則的價值衡量方法，展望了關(guān)聯(lián)規(guī)則挖掘的未來研究方向。關(guān)鍵詞數(shù)據(jù)挖掘,關(guān)聯(lián)規(guī)則,頻集,olapabstractthis paper provi...
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此文檔由會員 趙亮 發(fā)布
關(guān)聯(lián)規(guī)則挖掘綜述
頁數(shù) 9 字數(shù) 7791
摘要 本文介紹了關(guān)聯(lián)規(guī)則挖掘的研究情況，提出了關(guān)聯(lián)規(guī)則的分類方法，對一些典型算法進行了分析和評價，指出傳統(tǒng)關(guān)聯(lián)規(guī)則衡量標(biāo)準(zhǔn)的不足，歸納出關(guān)聯(lián)規(guī)則的價值衡量方法，展望了關(guān)聯(lián)規(guī)則挖掘的未來研究方向。
關(guān)鍵詞 數(shù)據(jù)挖掘,關(guān)聯(lián)規(guī)則,頻集,OLAP
Abstract This paper provides a survey of the study in association rule generation,brings forward a classification of association rule,reviews and analyses some typical algorithms,points out the weakness of the traditional measure method,concludes the measure method of the association rule’s value,views some future directions in association rule generation.
Key Words Data Mining, Association Rules, Large Itemset,OLAP
參考文獻
1 R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. Proceedings of the ACM SIGMOD Conference on Management of data, pp. 207-216, 1993.
2 Aly HH,Taha Y, Amr AA. Fast mining of association rules in large-scale problems.In:Abdel-Wahab H,Jeffay K,eds.Proc.of the 6th IEEE Symp.on Computers and
Communications(ISCC 2001).New York:IEEE Computer Society Press,2001.107-113.
3 Tsai CF,Lin YC,Chen CP.A new fast algorithms for mining association rules in large databases.In:Kamel AE,Mellouli K,Borne.P,eds.Proc.of the 2002 IEEE Int’1 Confon Systems, Man and Cybernetics(SMC 2002).IEEE Computer Soceity Press,2002.251-256.
4 S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic Itemset counting and implication rules for market basket data. In ACM SIGMOD International Conference On the Management of Data. 1997.
5 R Srik ant,R Agrawal.Mining generalized association rules[C].In:Proc of 21th Int’1 Conf on very Large Data Bases,Zurich,Switerland;Morgan Kaufmann,1995:407-419.
頁數(shù) 9 字數(shù) 7791
摘要 本文介紹了關(guān)聯(lián)規(guī)則挖掘的研究情況，提出了關(guān)聯(lián)規(guī)則的分類方法，對一些典型算法進行了分析和評價，指出傳統(tǒng)關(guān)聯(lián)規(guī)則衡量標(biāo)準(zhǔn)的不足，歸納出關(guān)聯(lián)規(guī)則的價值衡量方法，展望了關(guān)聯(lián)規(guī)則挖掘的未來研究方向。
關(guān)鍵詞 數(shù)據(jù)挖掘,關(guān)聯(lián)規(guī)則,頻集,OLAP
Abstract This paper provides a survey of the study in association rule generation,brings forward a classification of association rule,reviews and analyses some typical algorithms,points out the weakness of the traditional measure method,concludes the measure method of the association rule’s value,views some future directions in association rule generation.
Key Words Data Mining, Association Rules, Large Itemset,OLAP
參考文獻
1 R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. Proceedings of the ACM SIGMOD Conference on Management of data, pp. 207-216, 1993.
2 Aly HH,Taha Y, Amr AA. Fast mining of association rules in large-scale problems.In:Abdel-Wahab H,Jeffay K,eds.Proc.of the 6th IEEE Symp.on Computers and
Communications(ISCC 2001).New York:IEEE Computer Society Press,2001.107-113.
3 Tsai CF,Lin YC,Chen CP.A new fast algorithms for mining association rules in large databases.In:Kamel AE,Mellouli K,Borne.P,eds.Proc.of the 2002 IEEE Int’1 Confon Systems, Man and Cybernetics(SMC 2002).IEEE Computer Soceity Press,2002.251-256.
4 S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic Itemset counting and implication rules for market basket data. In ACM SIGMOD International Conference On the Management of Data. 1997.
5 R Srik ant,R Agrawal.Mining generalized association rules[C].In:Proc of 21th Int’1 Conf on very Large Data Bases,Zurich,Switerland;Morgan Kaufmann,1995:407-419.
